Markov Random Field (MRF) and Graph-Cut (2)
Deep dive into what energy functions can be minimized via graph-cut, with the necessary and sufficient condition f(0,0)+f(1,1)≤f(0,1)+f(1,0) and application to image segmentation.
Computing Life · An engineering notebook
An engineering notebook on AI, systems, and everyday experiments.
Long-form notes on agentic systems, engineering judgment, astrophotography, hardware, coffee, and the tools that make a life easier to inspect and improve.
Latest
383 posts · since 2012
-
381
-
382
Markov Random Field (MRF) and Graph-Cut (1)
Introducing how to formulate image restoration as a Markov Random Field optimization problem and solve it using graph-cut algorithm to achieve global optimal results efficiently.
-
383
No silver bullet in scientific computing
No single language fits all scientific computing needs. Following UNIX philosophy—combining C#, MATLAB, and Python through files and system calls—achieves both development and runtime efficiency.